What is k-fold cross-validation, and why is it used? Explain the process of k-fold cross-validation, including how the dataset is divided into 'k' subsets or folds, how models are trained and evaluated iteratively using these folds, and how performance metrics are aggregated. Discuss the advantages of k-fold cross-validation in estimating model performance and reducing bias compared to a simple train/test split. Additionally, highlight scenarios or situations where using a specific value of 'k' might be more suitable or less practical in the context of cross-validation.

K-fold cross-validation is a technique used to assess the performance of a model. It's particularly helpful for estimating how well a model will generalize to new, unseen data. The process involves dividing the dataset into 'k' subsets...